We are seeking an experienced Data Scientist to join the Entity & Actor Integrity team within Meta's Trust & Safety pillar. This team is responsible for protecting creators and users from account compromise and impersonation attacks. You will drive data-driven strategies to detect, prevent, and mitigate creator compromise and creator impersonation at scale, working closely with cross-functional partners to build safer experiences across Meta's platforms.
Responsibilities
Develop and refine detection models and metrics for identifying creator compromise and impersonation patterns at scale
• Partner with product, engineering, and policy teams to design and evaluate integrity interventions that protect creators
• Conduct deep-dive analyses to understand adversarial behaviors, attack vectors, and emerging threats targeting high-value accounts
• Define success metrics, build dashboards, and create measurement frameworks to track integrity health and intervention effectiveness
• Design and analyze experiments to optimize detection precision and user friction trade-offs
• Communicate insights and recommendations to leadership to inform Trust & Safety strategy and investment decisions
• Collaborate across integrity teams to identify cross-cutting opportunities and share learnings on actor-based abuse patterns
Minimum Qualifications
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
• 10+ years of experience in quantitative analysis, data science, or related analytical roles
• Experience with data querying languages (e.g., SQL) and scripting languages (e.g., Python, R)
• Experience communicating complex analytical findings to leadership and cross-functional stakeholders
• Experience leading ambiguous, cross-functional analytics projects with multiple stakeholders
Preferred Qualifications
• Familiarity with machine learning approaches for classification and anomaly detection
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience in Trust & Safety, integrity, fraud detection, or security-related analytics
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience with adversarial analysis, abuse detection, or account security domains
• Experience building and evaluating detection systems or intervention mechanisms
